Nuclear Genome Sequence Survey of the Dinoflagellate <i>Heterocapsa triquetra</i>
Bibliographic record
Abstract
Dinoflagellates have among the largest nuclear genomes known, but we know little about their contents or organisation. Given the interest in dinoflagellate ecology, cell biology, and evolutionary biology, there are many reasons to thoroughly investigate the contents of dinoflagellate genomes, but because of their large size the only thorough samples to date have relied on expressed sequence tag surveys to analyse cDNAs. To complement this, there are some studies of the physical properties of dinoflagellate chromosomes, but no direct survey of the nature of the sequences contained within them. To start to build a picture of the contents of these genomes, we have sequenced over 230,000 bp from the nuclear genome of Heterocapsa triquetra, which has been estimated to be 18-23 billion base pairs in total. The survey includes one putative gene with two relict spliced leaders, one putative pseudogene, and a small number of low-complexity repeats, transposons, and other putative selfish elements, all of which account for about 5% of the survey. Another 5% of the survey was long, complex repeats, some highly represented. By far the greatest fraction of the survey (89.5%) is made up of non-repeated sequence with no similarity to any other known sequence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".